Formulating the data-flow perspective for business process management

• 成果类型: Article
• 署名作者: Sun, Sherry X.; Zhao, J. Leon; Nunamaker, Jay E.; Sheng, Olivia R. Liu
• 署名单位: University of Arizona; Utah System of Higher Education; University of Utah
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.1060.0105
• 发表日期: 2006
• 页码: 374-391
• 关键词: workflow automation; verification; MODEL; support

摘要

Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling and analysis. One of the important aspects of workflow analysis is the data-flow perspective because, given a syntactically correct process sequence, errors can still occur during workflow execution due to incorrect data-flow specifications. However, there have been only scant treatments of the data-flow perspective in the literature and no formal methodologies are available for systematically discovering data-flow errors in a workflow model. As an indication of this research gap, existing commercial workflow management systems do not provide tools for data-flow analysis at design time. In this paper, we provide a data-flow perspective for detecting data-flow anomalies such as missing data, redundant data, and potential data conflicts. Our data-flow framework includes two basic components: data-flow specification and data-flow analysis; these components add more analytical rigor to business process management.